Multiple input multiple output (MIMO) communication systems, using multiple transmit and receive antennas, have become an important technology area in recent years. MIMO systems provide improved performance compared to single input single output (SISO) systems in the presence of multipath fading which characterize nearly all wireless communication channels.
In theory, using multiple transmit and receive antennas, transmit array gain can be achieved in addition to spatial diversity gain and receive array gain if the MIMO system operates in a closed-loop mode. Closed-loop MIMO allows for optimal transmit/receive beamforming. The beamforming may be done over any number of spatial modes of the channel. If beamforming is done over only the dominant mode of the channel, we call the corresponding beamforming vectors maximum ratio transmission (MRT) and maximum ratio combining (MRC) vectors for the transmit and receive sides, respectively. If beamforming is done over more than one spatial mode of the channel, it is referred to as linear pre-filtering or modal decomposition. Generally, a waterpouring algorithm is used in conjunction with modal decomposition algorithms. The term beamforming vectors will be used in association with MRT/MRC, while the term beamforming matrices will be used in association with modal decomposition.
In practice, approaching optimal transmit/receive beamforming over a communication channel is a function of the available knowledge of and accuracy of channel state information (CSI) at both ends (transmit and receive sides) of the channel. Channel estimation is concerned with estimating CSI. Accordingly, approaching optimal beamforming becomes a function of the quality of estimation schemes employed to estimate the communication channel. Known channel estimation techniques are typically partial CSI schemes. In other words, the feedback information from the receiver to the transmitter does not provide full knowledge of the transmission channel to the transmitter. Hence, partial CSI methods are insufficient for achieving optimal transmit/receive beamforming.
What is needed, therefore, are efficient techniques for achieving full CSI channel estimation that allow close to optimal transmit/receive beamforming.